Data analysis MAVEN PIZZA CHALLENGE Digital presence of your brand with Digital Rise solutions


    Improve operations and drive growth: A Data analysis Travel with Plato’s Pizza


    In the fast-paced and competitive world of the restaurant industry, Data analysis has become an indispensable tool for improving operations, improving customer experience and driving sales. Like a Data analysis specialist at Digital Rise Solutions, for Plato’s Pizza, a Greek-inspired pizzeria in New Jersey. The goal of this project was to leverage transactional data collected over the past year to uncover valuable insights, identify opportunities to drive more sales, and optimize operational efficiencies.

    Understanding the dataset:
    The dataset used in this project consists of four tables in CSV format:

    • Orders Table: Contains the date and time of all table orders placed.
    • Order Details table: Lists the different pizzas served with each order, along with their quantities.
    • Pizzas Table: Provides pizza size, price, and type information for each separate pizza in the Order Details table.
    • Pizza Types Chart: Contains complete details of pizza types, including name, category and ingredient list.

    The Maven Pizza Challenge: Plato’s Pizza, recognizing the potential of Data analysis . The challenge was to answer several critical questions using the dataset: Identify the busiest days and times: By analyzing the order graph, determine the peak days and times when the restaurant experiences the higher customer attendance. Understanding these trends could help Plato’s Pizza optimize its staff and resources during peak periods.

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    Evaluating peak hour pizza production: The Order Details table was key to quantifying the number of pizzas produced during peak periods. By doing so, the restaurant could ensure streamlined kitchen operations and minimize wait times for customers. Discover the best-selling and least-selling pizzas: The Pizzas and Pizza Types tables contained essential data to identify the best-selling pizzas, which could help focus on popular offerings and potentially promote them further. Additionally, knowing the least popular pizzas would allow the restaurant to consider possible improvements or remove items that don’t resonate with customers.

    Calculating Average Order Value: By analyzing the order table and associated pricing information, I calculated the average order value. This information allowed Plato’s Pizza to evaluate customer purchasing habits and adapt their marketing strategies to increase order value. Optimizing Seat Capacity Utilization: Understanding seat capacity utilization was vital for Plato’s Pizza. I evaluated order board data to determine how efficiently the restaurant used its 15 tables and 60 seats during different time periods, providing opportunities for improvement.

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